scholarly journals Persistent Homology of Geospatial Data: A Case Study with Voting

2019 ◽  
Author(s):  
Michelle Feng ◽  
Mason A. Porter
Keyword(s):  
Simplicial Complex ◽  
Persistent Homology ◽  
Point Clouds ◽  
Simplicial Complexes ◽  
Geospatial Data ◽  
Alternative Methods ◽  
Data Set ◽  
New Methods ◽  
Complex Construction

A crucial step in the analysis of persistent homology is the transformation of data into an appropriate topological object (in our case, a simplicial complex). Modern packages for persistent homology often construct Vietoris–Rips or other distance-based simplicial complexes on point clouds because they are relatively easy to compute. We investigate alternative methods of constructing these complexes and the effects of making associated choices during simplicial-complex construction on the output of persistent-homology algorithms. We present two new methods for constructing simplicial complexes from two-dimensional geospatial data (such as maps). We apply these methods to a California precinct-level voting data set, demonstrating that our new constructions can capture geometric characteristics that are missed by distance-based constructions. Our new constructions can thus yield more interpretable persistence modules and barcodes for geospatial data. In particular, they are able to distinguish short-persistence features that occur only for a narrow range of distance scales (e.g., voting behaviors in densely populated cities) from short-persistence noise by incorporating information about other spatial relationships between precincts.

scholarly journals SENSOR-TOPOLOGY BASED SIMPLICIAL COMPLEX RECONSTRUCTION FROM MOBILE LASER SCANNING

2018 ◽  
Vol IV-2 ◽  
pp. 121-128
Author(s):  
S. Guinard ◽  
B. Vallet
Keyword(s):  
Simplicial Complex ◽  
Laser Scanning ◽  
Point Clouds ◽  
Simplicial Complexes ◽  
Edge Length ◽  
New Method ◽  
Laser Beams ◽  
Local Geometry ◽  
3D Point Clouds ◽  
Complex Reconstruction

We propose a new method for the reconstruction of simplicial complexes (combining points, edges and triangles) from 3D point clouds from Mobile Laser Scanning (MLS). Our main goal is to produce a reconstruction of a scene that is adapted to the local geometry of objects. Our method uses the inherent topology of the MLS sensor to define a spatial adjacency relationship between points. We then investigate each possible connexion between adjacent points and filter them by searching collinear structures in the scene, or structures perpendicular to the laser beams. Next, we create triangles for each triplet of self-connected edges. Last, we improve this method with a regularization based on the co-planarity of triangles and collinearity of remaining edges. We compare our results to a naive simplicial complexes reconstruction based on edge length.

scholarly journals AUGMENTED REALITY FOR CULTURAL HERITAGE: THE REBIRTH OF A HISTORICAL SQUARE

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 303-308
Author(s):  
A. Scianna ◽  
G. F. Gaglio ◽  
M. La Guardia
Keyword(s):  
Augmented Reality ◽  
Rapid Prototyping ◽  
Cultural Heritage ◽  
Laser Scanner ◽  
Point Clouds ◽  
New Methods ◽  
Study Case ◽  
3D Information ◽  
Architectural Barriers

Abstract. The case study, faced in this paper, arises in the context of Interreg Italia-Malta European project named I-Access, dedicated to the improvement of accessibility to Cultural Heritage (CH). Accessibility considered not only as the demolition of physical architectural barriers, but also as the possibility of fruition of CH through technological tools that can increase its perception and knowledge. Last achievements in photogrammetry and terrestrial laser scanner (TLS) technology offered new methods of data acquisition in the field of CH, giving the possibility of monitoring and processing big data, in the form of point clouds. Ever in this field, reverse engineering techniques and computer graphics are even more used for involving visitors to discover CH, with navigation into 3D reconstructions, empowering the real visualization adding further 3D information through the Augmented Reality (AR). At the same time, recent advances on rapid prototyping technologies grant the automated 3D printing of scaled 3D model reconstructions of real CH elements allowing the tactile fruition of visitors that suffer from visual defects and the connection with 3D AR visualizations. The presented work shows how these technologies could revive an historical square, the Piazza Garraffo in Palermo (Italy), with the virtual insertion of its baroque fountain, originally placed there. The final products of this work are an indoor and an outdoor AR mobile application, that allow the visualization of the historical original asset of the square. This study case shows how the mixing of AR and the rapid prototyping technologies could be useful for the improvement of the fruition of CH. This work could be considered a multidisciplinary experimentation, where different technologies, today still in development, contribute to the same goal aimed at improving the accessibility of the monument for enhancing the fruition of CH.

NONLINEAR STATISTICS OF HUMAN SPEECH DATA

2009 ◽  
Vol 19 (07) ◽  
pp. 2307-2319 ◽  
Author(s):  
KENNETH A. BROWN ◽  
KEVIN P. KNUDSON
Keyword(s):  
Time Delay ◽  
Betti Number ◽  
Persistent Homology ◽  
Point Clouds ◽  
Simplicial Complexes ◽  
Data Sets ◽  
Human Speech ◽  
Speech Data ◽  
Nonlinear Statistics

We study the structure of point clouds obtained as time delay embeddings of human speech signals by approximating the data sets with certain simplicial complexes and analyzing their persistent homology. Results for several different sounds are presented in embedding dimensions 3 and 4. The first Betti number allows a coarse classification of sounds into three groups: vowels, nasals and noise.

scholarly journals HBIM MODELLING FOR AN HISTORICAL URBAN CENTRE

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 831-838
Author(s):  
M. Avena ◽  
E. Colucci ◽  
G. Sammartano ◽  
A. Spanò
Keyword(s):  
Central Italy ◽  
Point Clouds ◽  
Geospatial Data ◽  
Urban Centre ◽  
Semantic Data ◽  
Aerial Vehicle ◽  
3D Database ◽  
Data Structuring ◽  
The One

Abstract. The research in the geospatial data structuring and formats interoperability direction is the crucial task for creating a 3D Geodatabase at the urban scale. Both geometric and semantic data structuring should be considered, mainly regarding the interoperability of objects and formats generated outside the geographical space. Current reflections on 3D database generation, based on geospatial data, are mostly related to visualisation issues and context-related application. The purposes and scale of representation according to LoDs require some reflections, particularly for the transmission of semantic information.This contribution adopts and develops the integration of some tools to derive object-oriented modelling in the HBIM environment, both at the urban and architectural scale, from point clouds obtained by UAV (Unmanned Aerial Vehicle) photogrammetry.One of the paper’s objectives is retracing the analysis phases of the point clouds acquired by UAV photogrammetry technique and their suitability for multiscale modelling. Starting from UAV clouds, through the optimisation and segmentation, the proposed workflow tries to trigger the modelling of the objects according to the LODs, comparing the one coming from CityGML and the one in use in the BIM community. The experimentation proposed is focused on the case study of the city of Norcia, which like many other historic centres spread over the territory of central Italy, was deeply damaged by the 2016-17 earthquake.

scholarly journals Persistent Homology of Geospatial Data: A Case Study with Voting

SIAM Review ◽  
2021 ◽  
Vol 63 (1) ◽  
pp. 67-99
Author(s):  
Michelle Feng ◽  
Mason A. Porter
Keyword(s):  
Persistent Homology ◽  
Geospatial Data

scholarly journals Infrastructural Organization of Geospatial Data in The Global Level: A Case Study of Albanian Global Mapping Dataset

2020 ◽  
Vol 5 (1) ◽  
pp. 106
Author(s):  
Milot Lubishtani ◽  
Bashkim Idrizi ◽  
Subija Izeiroski ◽  
Fitore Bajrami Lubishtani
Keyword(s):  
Spatial Data ◽  
Spatial Data Infrastructure ◽  
Geospatial Data ◽  
Global Level ◽  
Data Set ◽  
Data Infrastructure ◽  
Creative Commons ◽  
Global Mapping ◽  
The Right

Today, the development of economic and financial situation concerning the protection of environment and natural resources in a wider scope depends on the use of geospatial data.  One of the main aims of the infrastructural organization of geospatial data is to provide users to be capable of acquiring complete, exact and updated dataset at the right time. This is necessary for providing an ideal environment, where all stakeholders can work collaboratively in an effective way, in order to solve environmental issues and to achieve their targets. Global Mapping (GM), a project established by United Nations, is one of the crucial contributions to the development of Global Spatial Data Infrastructure (GSDI). This case study on Albanian GM dataset was aimed at performing analyses of infrastructural organization of geospatial data in global-intercontinental level. Data standardization of GM as contributor of GSDI was analyzed through developed Albanian GM dataset. The main components taken into consideration for performing research analyses were data and metadata, technology, institutional framework, policies, interoperability, network services, search opportunities, and data sharing within GSDI. The main findings of this study are the necessity of infrastructural organization of geospatial data in the global level, known as GSDI, by including official geospatial datasets developed by the national mapping organizations of countries all over the world, in order to be used for environmental monitoring and protection, as well as for early warning management in international level. Finally, based on the research results, four conclusions for GSDI are offered, in order to be considered as guideline for further development of unified and globally homogeneous infrastructure of spatial data set. Keywords: GSDI; GM; spatial data infrastructure; Albania. Copyright (c) 2020 Geosfera Indonesia Journal and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals A sparsity preserving genetic algorithm for extracting diverse functional 3D designs from deep generative neural networks

2020 ◽  
Vol 6 ◽  
Author(s):  
James D. Cunningham ◽  
Dule Shu ◽  
Timothy W. Simpson ◽  
Conrad S. Tucker
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
Training Data ◽  
Alternative Methods ◽  
Data Set ◽  
Design Exploration ◽  
Latent Space

Generative neural networks (GNNs) have successfully used human-created designs to generate novel 3D models that combine concepts from disparate known solutions, which is an important aspect of design exploration. GNNs automatically learn a parameterization (or latent space) of a design space, as opposed to alternative methods that manually define a parameterization. However, GNNs are typically not evaluated using an explicit notion of physical performance, which is a critical capability needed for design. This work bridges this gap by proposing a method to extract a set of functional designs from the latent space of a point cloud generating GNN, without sacrificing the aforementioned aspects of a GNN that are appealing for design exploration. We introduce a sparsity preserving cost function and initialization strategy for a genetic algorithm (GA) to optimize over the latent space of a point cloud generating autoencoder GNN. We examine two test cases, an example of generating ellipsoid point clouds subject to a simple performance criterion and a more complex example of extracting 3D designs with a low coefficient of drag. Our experiments show that the modified GA results in a diverse set of functionally superior designs while maintaining similarity to human-generated designs in the training data set.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

2019 ◽  
Author(s):  
Niclas Ståhl ◽  
Göran Falkman ◽  
Alexander Karlsson ◽  
Gunnar Mathiason ◽  
Jonas Boström
Keyword(s):  
Reinforcement Learning ◽  
Short Term Memory ◽  
De Novo ◽  
De Novo Drug Design ◽  
Generative Process ◽  
New Methods ◽  
Multiparameter Optimization ◽  
Long Short Term Memory ◽  
New Compounds

<p>In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex and difficult multi-parameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modelled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improve these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output towards structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid, and a third satisfy the targeted objectives, while there were none in the initial set.</p>

Algebraic Shifting and Sequentially Cohen-Macaulay Simplicial Complexes

10.37236/1245 ◽  
1996 ◽  
Vol 3 (1) ◽  
Author(s):  
Art M. Duval
Keyword(s):  
Simplicial Complex ◽  
Simplicial Complexes ◽  
Pure Case ◽  
Definition Of ◽  
Algebraic Shifting

Björner and Wachs generalized the definition of shellability by dropping the assumption of purity; they also introduced the $h$-triangle, a doubly-indexed generalization of the $h$-vector which is combinatorially significant for nonpure shellable complexes. Stanley subsequently defined a nonpure simplicial complex to be sequentially Cohen-Macaulay if it satisfies algebraic conditions that generalize the Cohen-Macaulay conditions for pure complexes, so that a nonpure shellable complex is sequentially Cohen-Macaulay. We show that algebraic shifting preserves the $h$-triangle of a simplicial complex $K$ if and only if $K$ is sequentially Cohen-Macaulay. This generalizes a result of Kalai's for the pure case. Immediate consequences include that nonpure shellable complexes and sequentially Cohen-Macaulay complexes have the same set of possible $h$-triangles.

Parent and Grandparent Relationships in Emerging Adulthood

2018 ◽  
Author(s):  
Michael W. Pratt ◽  
M. Kyle Matsuba
Keyword(s):  
Emerging Adulthood ◽  
Attachment Theory ◽  
Emerging Adults ◽  
Family Relationships ◽  
Longitudinal Research ◽  
Study Data ◽  
Family Development ◽  
Data Set ◽  
Theory Framework

Chapter 7 begins with an overview of Erikson’s ideas about intimacy and its place in the life cycle, followed by a summary of Bowlby and Ainsworth’s attachment theory framework and its relation to family development. The authors review existing longitudinal research on the development of family relationships in adolescence and emerging adulthood, focusing on evidence with regard to links to McAdams and Pals’ personality model. They discuss the evidence, both questionnaire and narrative, from the Futures Study data set on family relationships, including emerging adults’ relations with parents and, separately, with grandparents, as well as their anticipations of their own parenthood. As a way of illustrating the key personality concepts from this family chapter, the authors end with a case study of Jane Fonda in youth and her father, Henry Fonda, to illustrate these issues through the lives of a 20th-century Hollywood dynasty of actors.

Sign in / Sign up

Export Citation Format

Share Document